21results about How to "Reduce Data Transfer Latency" patented technology

This invention discloses an intelligent early warning system and method for railway line patrol safety based on edge computing, involving intelligent safety early warning in the railway transportation field, enabling real-time controllable emergency measures to minimize the negative impact of incidents. The edge computing-based intelligent early warning system for railway line patrol safety includes: a cloud platform for an intelligent patrol system along the railway line, network infrastructure units for the intelligent patrol system along the railway line, on-site edge intelligent early warning devices and traffic control units, and IoT video sensing infrastructure units. This invention aims to deploy edge computers to continuously analyze 24-hour video streams from surveillance cameras at key locations along the railway line, process data in real-time, reduce latency, promptly identify abnormal events, take temporary emergency measures as quickly as possible, and rapidly report abnormal events, effectively shortening response time, significantly improving the efficiency of handling abnormal events in railway transportation, enhancing the accuracy of emergency measures, and ensuring railway line safety.

The invention relates to the technical field of disaster early warning processing, in particular to a mountain torrent disaster early warning system based on deep learning and air-space-ground integrated perception. The system comprises the following modules: a space-air-ground sensing module, an edge calculation preprocessing module, a deep learning prediction module and an intelligent decision and early warning module. Wherein the air-space-ground sensing module is used for collecting multi-source data; wherein the multi-source data comprises earth surface deformation data obtained through a spaceborne synthetic aperture radar, high-precision topographic data obtained through an unmanned aerial vehicle laser radar, hydrological sensing data obtained through a ground internet-of-things distributed sensor, and meteorological numerical forecasting data obtained through a meteorological data interface. Through air-space-ground integrated perception, physical constraint deep learning and edge intelligence technologies, mountain torrent multi-source data collaborative acquisition and accurate mountain torrent disaster prediction are realized, so that the comprehensiveness, reliability and timeliness of mountain torrent disaster early warning are improved.

This invention relates to the field of model update and switching technology, and particularly to a method for hot model update and intelligent switching under a cloud-edge collaborative architecture. The method includes: performing change detection on a stored list of model versions via a cloud platform, and generating an update instruction containing an identifier of the target updated model version based on the change detection results; retrieving the model update file from the model repository based on the update instruction; loading the model update file in a local cache area; switching the current model version to the new model version corresponding to the model file in the pending switch state while the current model version is running on the target edge node; monitoring the real-time running status of the new model version on the target edge node; and switching back to the current model version when the new model version's running status is abnormal. This invention enables dynamic hot updates and intelligent switching of model versions in a cloud-edge collaborative scenario, improving the efficiency of model deployment.

The invention belongs to the technical field of intelligent identification and detection, and discloses an intelligent identification terminal adaptive to the Internet of Things, and the terminal comprises a housing; the data acquisition module is used for synchronously acquiring behavior inertial data and visual image data of a target animal; an edge calculation module which is in communication connection with the data acquisition module and is used for operating the trained behavior identification model and visual representation identification model and performing real-time processing and identification on the acquired data; the information fusion and decision module is integrated on the edge calculation module; and the Internet of Things communication module is used for uploading the state judgment conclusion and the terminal data to a remote management platform. According to the scheme, inertial motion and visual image data are synchronously collected, and real-time processing and information fusion are carried out on the terminal locally by utilizing edge calculation and an AI model, so that the accuracy, the real-time performance and the environmental adaptability of animal state recognition are effectively improved.

The invention relates to the field of acquisition and transmission data analysis, in particular to an acquisition and transmission data comparative analysis method based on parameter real-time monitoring. The technical problems that in the prior art, most of collected and transmitted data comparative analysis methods depend on manual data analysis, lack intelligent data processing and exception recognition capabilities, lack advanced algorithm support, incomplete data models and limitation of system architecture, and consequently complex data analysis and processing tasks are difficult to complete automatically are solved; according to the technical scheme, the acquisition and transmission data comparative analysis method based on parameter real-time monitoring comprises the steps that a parameter threshold value and a standard range are set, comparative analysis is conducted on real-time data and historical data, and abnormal points and trend changes in the data are recognized; according to the method, the data is sorted and sorted according to the time sequence, the real-time data and the historical data are compared and analyzed, the influence of interference factors on the data is eliminated through an algorithm model, and the identified abnormal data and trend are analyzed to realize automatic analysis of the data.

The application relates to the technical field of power monitoring, and discloses a power operation data monitoring method and system based on a cloud network, the method comprising the following steps: acquiring power equipment operation state data flow, uploading the operation state data flow to an edge intelligent gateway; the edge intelligent gateway acquires an abnormality index determined according to the operation state data flow, and uploads the operation state data flow to a corresponding cloud network resource pool in stages according to the abnormality index; after the cloud network resource pool receives the operation state data flow, the cloud network resource pool is elastically expanded according to the abnormality index. Through the method and the corresponding system, the matching between resources and data importance can be ensured, the insufficient computing power can be avoided, and the cloud network resource pool can be dynamically allocated on demand to improve resource utilization.

The application discloses a data transmission method and device, equipment and medium, comprising: acquiring user posture data sent by a VR device; generating a picture frame to be displayed on the VR device based on the user posture data; and transmitting the picture frame to the VR device based on an SRT protocol. In this way, the picture frame to be displayed on the VR device is generated based on the user posture data acquired from the VR device, and then the picture frame is transmitted to the VR device based on the SRT protocol, so that the delay of data transmission between the computer device and the VR device can be reduced under the premise of ensuring reliable transmission.

The application discloses a kind of inspection robot charging control system and charging device thereof, it is related to inspection robot technical field, and the optimal balance between the charging demand and task execution of inspection robot is realized by dynamic task and charging scheduling algorithm: arranging internet of things sensor in the inspection area, the position, electric quantity, current task state and environmental condition etc.data of robot are monitored in real time;And edge computing node is deployed in the inspection area, sensor data is handled and analyzed in real time, to reduce the burden of central server, reduce data transmission delay;Based on the characteristics of inspection task and the activity trajectory of robot, charging point is flexibly arranged in the inspection area;Through internet of things data analysis, the position and quantity of charging point are dynamically adjusted, to avoid the return problem caused by fixed charging point;Mobile charging site goes to the position of robot according to the position and electric quantity state of robot and charges actively, to reduce the return time of robot.

The invention discloses a lightweight scene text recognition method for a low-quality image, and relates to the technical field of scene text recognition, and the method comprises the steps: 1, calculating the quality perception domain distance of each sample image in a collected training data set, and dividing the training data set into a plurality of batches of data sets according to the quality perception domain distance; 2, constructing a lightweight scene text recognition model, wherein the lightweight scene text recognition model comprises a lightweight feature extraction backbone network and a sequence decoding module which are connected in series; step 3, training the lightweight scene text recognition model by using the plurality of batches of data sets and adopting a hierarchical training strategy to obtain a trained lightweight scene text recognition model; and step 4, inputting the collected to-be-recognized image into the trained lightweight scene text recognition model to generate a recognition result. According to the method, lightweight feature extraction, quality perception domain adaptation and progressive layered fine tuning are integrated, so that the efficiency, robustness and generalization ability of image text recognition are improved.

This application provides a data processing method, apparatus, and electronic device based on artificial intelligence of things (AIIoT), relating to the field of intelligent lighting control technology. The method involves: acquiring the operational plan within the target lighting area of ​​a port; monitoring operational and environmental data in real time; comparing and analyzing the operational plan with the operational data to obtain a comparison analysis result; selecting an initial lighting strategy matching the comparison analysis result from a preset strategy library based on the comparison analysis result; adjusting the initial lighting strategy using environmental data; when the adjustment magnitude exceeds a preset magnitude threshold, the smart cloud box uploads the initial lighting strategy and environmental data to a cloud platform, where the cloud platform adjusts the strategy to obtain the target lighting strategy and distributes it to the smart cloud box; when the adjustment magnitude does not exceed the preset magnitude threshold, the smart cloud box uses the adjusted lighting strategy as the target lighting strategy and distributes it to the light pole terminal, enabling timely adjustment of the lighting strategy.

The application discloses a JSBSim dynamics model plug-in for UE4 cluster simulation, belongs to the technical field of unmanned aerial vehicle cluster flight simulation, and comprises an initialization module, a data interaction layer module, a simulation cycle module and a UE4 cluster simulation system module; the initialization module is responsible for constructing a simulation basic environment; the data interaction layer module realizes bidirectional data flow; the simulation cycle module drives a simulation process at a fixed frequency; the UE4 cluster simulation system module takes an actor as a physical carrier, converts coordinates of data output by JSBSim into coordinates and rotation values in UE4 local coordinates, updates position and posture information of the actor in real time, and presents simulation results in a visualized manner; the JSBSim dynamics model plug-in for UE4 cluster simulation provided by the application solves the problems of low dynamics simulation precision of an existing cluster simulation system and the problem that an official plug-in of JSBSim only supports UE5.

This invention relates to the field of energy information processing technology, specifically to a large-scale model-based big data anti-congestion optimization system for energy and power planning. The system includes: a data acquisition and link status awareness module for generating link status indicators; a channel load prediction and overload identification module for comparing predicted load values ​​with capacity thresholds to identify data channels at risk of overload; a large-scale model path decision module for generating optimized data distribution paths for raw data packets; a dynamic data flow scheduling module for diverting and scheduling data flows and adjusting relevant data processing resource configurations; and a strategy feedback model update module for training and updating the large-scale language model using feedback information. This invention achieves dynamic diversion scheduling and path optimization for big data in energy and power planning, improving data transmission efficiency and scheduling stability under high-load scenarios.

The application discloses a kind of based on digital ray technology's transmission line strain clamp nondestructive testing method, it is related to transmission line strain clamp detection field, method includes: collection strain clamp radiographic image and geometric dimension, stress and so on multidimensional parameter, data fusion is carried out by hypergraph structure, multidimensional heterogeneous strain clamp analysis model is calculated and analyzed in edge side using optimized hypergraph neural network and edge computing gateway, to visualize presentation detection result in the form of double screen linkage, while realizing intelligent diagnosis and defect early warning;The application provides an efficient, accurate and reliable solution for the intelligent operation and maintenance of transmission line strain clamp, effectively improves the safe operation level and operation and maintenance efficiency of power grid.

This disclosure provides a data transmission apparatus, a data transmission method, and an electronic device. The data transmission apparatus includes a clock generation module, a phase selection module, and a data transmission module. The clock generation module is configured to sequentially generate N sampling result clock signals with uniformly distributed phases. The phase selection module is configured to determine a target sampling result clock signal based on the phase relationship between the N uniformly distributed sampling result clock signals and a second low-speed clock. The data transmission module is configured to sample source data based on the target sampling result clock signal to generate intermediate transmission data, and then sample the intermediate transmission data based on the second low-speed clock to obtain the target data. This data transmission apparatus enables efficient data transmission across clock domains, simplifies the data transmission path, has lower data transmission latency, significantly reduces chip area and power consumption, and improves data transmission reliability.

The utility model discloses an Internet of Things gateway with multiple communication fusion and intelligent topology construction functions, and relates to the technical field of gateways. An Internet of Things gateway with multiple communication fusion and intelligent topology construction functions comprises a display screen, a shell, a top mainboard, a middle mainboard and a bottom mainboard, the display screen is embedded in the middle of the upper portion of the shell, the display screen is electrically connected with the top mainboard, the middle mainboard and the bottom mainboard, and the top mainboard, the middle mainboard and the bottom mainboard are arranged in the shell. An overvoltage, overcurrent, overload and surge protection circuit is built in the gateway, all-around safety protection is provided for equipment, communication modes of Wi-Fi, Bluetooth, 4G, LoRa, Zigbee, Thread, Ethernet and the like can be flexibly switched by the gateway through multi-communication technology fusion, communication requirements of equipment in different scenes are met, a sensor network of an automatic mesh topology structure is constructed, and when a node fails or signal interference occurs, the communication mode of the equipment in different scenes can be automatically switched. And data can be automatically forwarded through other nodes, so that the network reliability and fault tolerance are greatly improved.

This invention provides a method and system for redundant data processing and transmission in FPGA-ROS, relating to the field of robot operating system communication. The method executes input and output processing flows in parallel within the FPGA: in the input flow, newly received ROS topics are compared with historical topics; if the content differs, it is stored in main memory; otherwise, it is discarded. In the output flow, topics are read from main memory and sent according to a preset mode. In the non-timing mode, a second comparison is performed before transmission to filter redundancy, and topics oriented towards the executor can be translated into instruction format. This invention implements dual redundancy filtering and instruction conversion in hardware, significantly reducing storage and bandwidth overhead and improving system processing efficiency and compatibility.

The utility model relates to intelligent electric energy meter technical field discloses a kind of intelligent electric energy meter based on dual-mode communication, including main controller, dual-mode communication module and mode control circuit;Main controller includes first communication port and mode control end;Dual-mode communication module has first communication mode and second communication mode;Dual-mode communication module includes second communication port, power end and enable end, and second communication port is connected with first communication port;Mode control circuit includes switch tube, and the control end of switch tube is connected with mode control end, and the first connecting end of switch tube is connected with enable end, and the second connecting end of switch tube is connected with power end;Wherein, dual-mode communication module is selectively operated in first communication mode or second communication mode according to the level state of first connecting end output.The utility model can effectively reduce the communication delay of electric energy meter under complex power environment.

The application discloses an image recognition method, product, equipment and medium based on data multiplexing, and is applied to an image recognition circuit comprising multiple sets of recognition sub-circuits; comprising: storing initial image data of an original image to be recognized in a preset global cache; controlling a convolution window of an image recognition CNN to slide in the initial image data; each recognition sub-circuit corresponds to a different convolution window, and comprises a selector, a data register and a memristor array; the selector is used for acquiring non-multiplexed and multiplexed target data from the preset global cache and a data register corresponding to a previous convolution window respectively; the target data is stored in the data register as convolution data to be convolved of a current convolution window, the convolution data to be convolved is processed by the memristor array to obtain a convolution result of the current convolution window; and a feature map of the original image to be recognized is generated according to the convolution results of the convolution windows to perform image recognition. Transmission delay and hardware power consumption in the image recognition process are reduced.

The application discloses an intelligent electric appliance cabinet management and control method applied to an intelligent electric appliance cabinet management and control system, and comprises a data collection module, an intelligent monitoring module and a communication management module, characterized by that the data collection module is used for collecting identity authentication data of management personnel and comprehensive monitoring data of electric equipment; the intelligent monitoring module is used for monitoring whether there is a stranger around the electric appliance cabinet and verifying identity information of an electric appliance cabinet operator; the communication management module is used for managing a communication channel and a communication node of the electric appliance cabinet and reducing communication time delay of the electric appliance cabinet; the data collection module, the intelligent monitoring module and the communication management module are mutually communicated and connected; the data collection module comprises a data acquisition module, a visual module and a sensor module; the data acquisition module is used for collecting setting data of the electric appliance cabinet; and the visual module is used for collecting visual images around the electric appliance cabinet. The application has the characteristics of high safety and low time delay.

This application relates to the field of natural gas processing technology, specifically to an integrated natural gas separator device. The device includes a separator, valve assembly, level gauge, control unit, sensing unit, electric valve, and audible and visual alarm device. The separator is composed of a dual-cylinder separator, a slug trap, and a flash separator connected in sequence. The valve assembly includes a control valve installed on the separator's inlet pipe, a shut-off valve on the outlet pipe, and a bypass valve on the drain pipe. The level gauge includes a magnetic level gauge and a radar level gauge, respectively installed in the separator's level observation window and on its top. The control unit is connected to the level gauge, sensing unit, and electric valve. The sensing unit includes a pressure sensor and a flow sensor, respectively installed on the separator's inlet and outlet pipes. The electric valve is installed on the drain pipe and connected to the control unit. The audible and visual alarm device is installed externally to the device and connected to the control unit.

The invention discloses a high-dust closed storage environment three-dimensional reconstruction system based on an edge-cloud collaborative architecture, and relates to the technical field of intelligent storage. The objective of the invention is to solve the problems that the real-time performance and integrity of data transmission are difficult to guarantee and system resources are wasted in an existing silo scanning system. The system comprises a plurality of intelligent sensing terminals and a cloud management platform, wherein the plurality of intelligent sensing terminals can communicate with the cloud management platform; the intelligent sensing terminal obtains distance information between a target and an observation point through radar information, fuses the distance information and a radar attitude angle into a data frame, and sends the data frame to the cloud management platform; and the cloud management platform analyzes the received data frame to obtain a point cloud data stream, calculates a target coordinate according to the point cloud data stream by adopting a point cloud processing algorithm, and further constructs a three-dimensional model.